中国电化教育

深度学习与社会性学习紧密关联，社会性支持机制的缺失是制约深度学习发展的重要因素。有效开展深度学习需要对社会性支持服务样态进行系统审思与构建。该研究聚焦学习的社会性本质，从学习者、知识、学习过程三个维度深入阐释社会性支持的关键需求，在此基础上构建了以社会知识网络为载体的社会性支持多层次渐进表征与聚合模型。该模型涵盖“社会性特征的可感知”“社会性知识的可获取”“社会性活动的可参与”“社会性知识的可分享”“社会性关系的可发展”“社会性群体的可加入”“社会性知识的可建构”七个核心要素，面向深度学习的发展过程形成了一个动态化、递进式的支持框架。在此模型的指导下，设计开发了社会知识网络工具SKN，通过结构化地聚合和组织多维社会性节点，为深度学习的信息输入、活动参与、知识创生三个关键阶段提供适应性的社会性支持服务，为优化深度学习的社会性支持机制提供了理论依据和实践参考。

Deep learning is closely related to social learning, and the absence of social support constitutes a significant constraint on the development of deep learning. The effective implementation of deep learning necessitates systematic reflection and construction of social support service modalities. This article focuses on the social nature of learning, thoroughly explicating the critical requirements for social support across three dimensions: learners, knowledge, and learning processes. Based on this foundation, establishing a multilevel progressive representation and aggregation model of social support, and utilizing social knowledge networks as the carrier. The model encompasses seven core elements:perceptibility of social characteristics, accessibility of social knowledge, participability of social activities, shareability of social knowledge,developability of social relationships, joinability of social groups and constructability of social knowledge. These elements form a dynamic,progressive support framework oriented toward the developmental process of deep learning. Guided by this model, the Social Knowledge Network(SKN) tool was designed and developed. Through structurally aggregating and organizing multidimensional social nodes, SKN provides adaptive social support services for three critical stages of deep learning: information input, activity participation, and knowledge generation. This article provides both theoretical foundations and practical references for optimizing social support mechanisms in deep learning.